Please use this identifier to cite or link to this item: http://hdl.handle.net/11718/19606
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dc.contributor.authorSinha, Ankur
dc.contributor.authorMalo, P.
dc.contributor.authorDeb, K.
dc.date.accessioned2017-06-23T04:16:13Z
dc.date.available2017-06-23T04:16:13Z
dc.date.issued2017
dc.identifier.citationEuropean Journal of Operational Research,Volume 257(2)2017, Pages 395-411en_US
dc.identifier.urihttp://hdl.handle.net/11718/19606
dc.description.abstractBilevel optimization problems are a class of challenging optimization problems, which contain two levels of optimization tasks. In these problems, the optimal solutions to the lower level problem become possible feasible candidates to the upper level problem. Such a requirement makes the optimization problem difficult to solve, and has kept the researchers busy towards devising methodologies, which can efficiently handle the problem. Despite the efforts, there hardly exists any effective methodology, which is capable of handling a complex bilevel problem. In this paper, we introduce bilevel evolutionary algorithm based on quadratic approximations (BLEAQ) of optimal lower level variables with respect to the upper level variables. The approach is capable of handling bilevel problems with different kinds of complexities in relatively smaller number of function evaluations. Ideas from classical optimization have been hybridized with evolutionary methods to generate an efficient optimization algorithm for a wide class of bilevel problems. The performance of the algorithm has been evaluated on two sets of test problems. The first set is a recently proposed SMD test set, which contains problems with controllable complexities, and the second set contains standard test problems collected from the literature. The proposed method has been compared against three benchmarks, and the performance gain is observed to be significant.en_US
dc.language.isoen_USen_US
dc.publisherElsevier B.V.en_US
dc.subjectBilevel optimizationen_US
dc.subjectEvolutionary algorithmsen_US
dc.subjectQuadratic approximationsen_US
dc.titleEvolutionary algorithm for bilevel optimization using approximations of the lower level optimal solution mappingen_US
dc.typeArticleen_US
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